Association between ataxia telangiectasia mutated gene polymorphisms and breast cancer in Taiwanese females.
Bibliographic record
Abstract
AIM: Several epidemiological studies have investigated the association between ataxia telangiectasia mutated (ATM) gene polymorphisms and breast cancer risk. However, published data are still inconclusive and there are no such studies for Taiwan. Thus, the polymorphic variants of ATM were investigated for their association with breast cancer in Taiwan for the first time here. PATIENTS AND METHODS: In this hospital-based matched case-control study, associations of seven ATM single nucleotide polymorphisms (rs600931, rs652311, rs227060, rs227292, rs624366 and rs189037) with breast cancer risk in a Taiwanese population were investigated. One thousand two hundred and thirty-two patients with breast cancer and the same number of age-matched healthy controls recruited were genotyped and analyzed. RESULTS: There was a slight difference between breast cancer and control groups in the distributions of their genotypic (p = 0.0774) and allelic frequencies (p = 0.0217) in the rs189037 polymorphism. As for the other six polymorphisms there was no differential distribution. CONCLUSION: Our data indicate that ATM polymorphism is associated with breast cancer, and the A allele of ATM rs189037 is a minor risky biomarker of breast cancer in Taiwan. The gene-gene and gene-environment interactions of ATM with other factors is worthy of further investigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".